{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d153f048-0fcf-4e66-b23e-94ee35cd6570",
   "metadata": {},
   "source": [
    "# 1. 加载模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "48b19032-4ba8-445a-91bf-2a874a9951f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import Qwen2VLForConditionalGeneration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "24d3dd45-f008-4306-ae19-1a6aed2fcd4d",
   "metadata": {
    "ExecutionIndicator": {
     "show": true
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "from transformers import AutoTokenizer, AutoProcessor\n",
    "from qwen_vl_utils import process_vision_info\n",
    "from modelscope import snapshot_download\n",
    "import torch\n",
    "import os\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7c076aef-1703-47f0-9daa-f91be727aa85",
   "metadata": {
    "ExecutionIndicator": {
     "show": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "root_dir = \"/\"\n",
    "# 魔搭train-data路径\n",
    "# train_path = os.path.join(root_dir, \"mnt\", \"workspace\", \"datasets\", \"doraemon-bot\", \"train\")\n",
    "# 趋动云train-data路径\n",
    "code_path = os.path.join(root_dir, \"gemini\", \"code\")\n",
    "model_path = os.path.join(root_dir, \"gemini\", \"code\", \"pretrain-sft2\")\n",
    "train_path = os.path.join(root_dir, \"gemini\", \"data-2\")\n",
    "image_path = os.path.join(train_path, \"images\")\n",
    "\n",
    "train_file_fullpath = os.path.join(code_path, \"datasets\", \"test1_fullpath.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6ad6fc3a-e8a1-494f-a832-629885ca9962",
   "metadata": {
    "ExecutionIndicator": {
     "show": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Qwen2VLRotaryEmbedding` can now be fully parameterized by passing the model config through the `config` argument. All other arguments will be removed in v4.46\n",
      "Loading checkpoint shards: 100%|██████████| 4/4 [05:06<00:00, 76.60s/it]\n"
     ]
    }
   ],
   "source": [
    "# 加载预训练模型和处理器到可用设备（CPU或GPU）\n",
    "try:\n",
    "    # 加载预训练模型和处理器到可用设备（CPU或GPU）\n",
    "    model = Qwen2VLForConditionalGeneration.from_pretrained(\n",
    "        model_path, torch_dtype=torch.float16, device_map=\"auto\"\n",
    "    )\n",
    "    processor = AutoProcessor.from_pretrained(model_path)\n",
    "except Exception as e:\n",
    "    print(f\"加载模型时出错: {e}\")\n",
    "    raise"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "681bcff7-4879-474d-93ec-b5776b76da31",
   "metadata": {},
   "source": [
    "# 2. 遍历数据获取结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ad03d001-a646-421c-a2dc-0aa408ef1370",
   "metadata": {},
   "outputs": [],
   "source": [
    "# def prepare_inputs_for_inference(messages, max_images=1, target_size=(800, 800)):\n",
    "def prepare_inputs_for_inference(messages):\n",
    "    \"\"\"\n",
    "    将消息转换为模型可以接受的输入格式。\n",
    "    \n",
    "    :param messages: 消息列表，包含系统提示和用户输入\n",
    "    :return: 模型输入张量\n",
    "    \"\"\"\n",
    "    text_template = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\n",
    "    image_inputs, _ = process_vision_info(messages)\n",
    "    inputs = processor(text=[text_template], images=image_inputs, padding=True, return_tensors=\"pt\").to(\"cuda\")\n",
    "    return inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a89488f0-3e30-4478-a1b2-c727613548c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_output(inputs):\n",
    "    \"\"\"\n",
    "    使用模型生成输出文本。\n",
    "    \n",
    "    :param inputs: 模型输入张量\n",
    "    :return: 解码后的输出文本\n",
    "    \"\"\"\n",
    "    generated_ids = model.generate(**inputs, max_new_tokens=256)\n",
    "    generated_ids_trimmed = [out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]\n",
    "    output_text = processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]\n",
    "    return output_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c5abbcd4-58d4-4455-9ebe-a5031cd4c5fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_json_response(output_text):\n",
    "    \"\"\"\n",
    "    尝试将模型的输出文本解析为JSON格式。\n",
    "    \n",
    "    :param output_text: 模型生成的输出文本\n",
    "    :return: 如果成功解析则返回字典对象，否则返回None\n",
    "    \"\"\"\n",
    "    try:\n",
    "        return json.loads(output_text)\n",
    "    except json.JSONDecodeError:\n",
    "        print(f\"json 解析错误：{output_text}\")\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "72f6696b-1968-4ee4-a139-2fb8d23b25bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cleanup_cuda_cache():\n",
    "    \"\"\"清理CUDA缓存以释放显存\"\"\"\n",
    "    torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "89aeeffc-5ea0-4385-9d4d-1cf07b82864c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data_from_json(file_path):\n",
    "    \"\"\"\n",
    "    从指定路径加载JSON文件内容。\n",
    "    \n",
    "    :param file_path: JSON文件的路径\n",
    "    :return: JSON解析后的内容，如果文件不存在或解析失败则返回None\n",
    "    \"\"\"\n",
    "    try:\n",
    "        with open(file_path, 'r', encoding='utf-8') as file:\n",
    "            content = file.read().strip()\n",
    "            if not content:\n",
    "                print(\"文件为空\")\n",
    "                return None\n",
    "            return json.loads(content)\n",
    "    except (FileNotFoundError, json.JSONDecodeError) as e:\n",
    "        print(f\"加载文件 {file_path} 出现问题: {e}\")\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "55df786f-ddd4-474e-a45d-23880ae4a9cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_model_response(strid, text, image_filepath):\n",
    "    \"\"\"\n",
    "    根据文本和图片路径获取模型的推理结果，并尝试解析为JSON格式。\n",
    "    \n",
    "    :param text: 用户输入的文本信息\n",
    "    :param image_filepath: 图片文件的绝对路径\n",
    "    :return: 包含模型输出的字典对象，如果解析失败则返回None\n",
    "    \"\"\"\n",
    "    # sys_prompt = \"\"\"\n",
    "    # 请对用户输入的内容和图片进行分析！将分析结果输出为JSON，key是output，value是分析结果，如：\n",
    "    # {\n",
    "    #     \"output\": \"分析结果\"\n",
    "    # }\n",
    "    # \"\"\"\n",
    "    # 判断是否提供了有效的图片路径，对于空图片的样本，不传图片标签。\n",
    "    has_image = bool(image_filepath and image_filepath.strip())\n",
    "    images = []\n",
    "    if has_image:\n",
    "        images = [{\"type\": \"image\", \"image\": image_filepath}]\n",
    "    messages = [\n",
    "        # {\"role\": \"system\", \"content\": sys_prompt},\n",
    "        {\n",
    "            \"role\": \"user\",\n",
    "            \"content\": \n",
    "                [{\"type\": \"text\", \"text\": text}] + images\n",
    "        }\n",
    "    ]\n",
    "\n",
    "    try:\n",
    "        # 准备文本模板和视觉信息用于推理\n",
    "        inputs = prepare_inputs_for_inference(messages)\n",
    "        # 推理并生成响应\n",
    "        with torch.no_grad():  # 禁用梯度计算以节省内存\n",
    "            output_text = generate_output(inputs)\n",
    "        # parsed_response = parse_json_response(output_text)\n",
    "        cleanup_cuda_cache()\n",
    "        # return parsed_response\n",
    "        return output_text\n",
    "    \n",
    "    except Exception as e:\n",
    "        print(f\"推理过程中发生错误: {e}\")\n",
    "        cleanup_cuda_cache()\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f1ea47fa-12be-48fd-9e52-fd40c6732e1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "cleanup_cuda_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "83ead02f-db1f-447c-b7dd-f8bfb2fb1f8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 初始化变量\n",
    "results = []\n",
    "num_errors = 0\n",
    "# 加载训练数据\n",
    "data = load_data_from_json(train_file_fullpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "70dcb769-138c-4976-bb72-8c1fd88ca638",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import os\n",
    "from tqdm import tqdm\n",
    "\n",
    "# 假设 get_model_response 函数和 data, image_path 已经定义好了\n",
    "\n",
    "# JSON 文件路径\n",
    "json_file_path = 'results.json'\n",
    "\n",
    "def save_to_json(new_result):\n",
    "    # 读取现有的结果列表或初始化为空列表\n",
    "    if os.path.exists(json_file_path):\n",
    "        with open(json_file_path, mode='r', encoding='utf-8') as file:\n",
    "            results = json.load(file)\n",
    "    else:\n",
    "        results = []\n",
    "\n",
    "    # 添加新的结果到列表中\n",
    "    results.append(new_result)\n",
    "\n",
    "    # 将更新后的结果列表写回JSON文件\n",
    "    with open(json_file_path, mode='w', encoding='utf-8') as file:\n",
    "        json.dump(results, file, ensure_ascii=False, indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81d83376-ac50-48d0-b8fc-6f50ef357388",
   "metadata": {
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      "['/gemini/data-2/images/2107aafc17275075590991184d100c-0.jpg', '/gemini/data-2/images/2107aafc17275075590991184d100c-1.jpg']\n",
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      "['/gemini/data-2/images/2132d91b17276822460003292d0b16-0.jpg']\n",
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      "推理过程中发生错误: NVML_SUCCESS == DriverAPI::get()->nvmlDeviceGetHandleByPciBusId_v2_( pci_id, &nvml_device) INTERNAL ASSERT FAILED at \"../c10/cuda/CUDACachingAllocator.cpp\":980, please report a bug to PyTorch. \n"
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      "['/gemini/data-2/images/21509c5117275679757331057d07e9-0.jpg', '/gemini/data-2/images/21509c5117275679757331057d07e9-1.jpg']\n",
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      "推理过程中发生错误: NVML_SUCCESS == DriverAPI::get()->nvmlDeviceGetHandleByPciBusId_v2_( pci_id, &nvml_device) INTERNAL ASSERT FAILED at \"../c10/cuda/CUDACachingAllocator.cpp\":980, please report a bug to PyTorch. \n",
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      "['/gemini/data-2/images/0b51d00517272755524115512d0ffc-0.jpg', '/gemini/data-2/images/0b51d00517272755524115512d0ffc-1.jpg']\n",
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      "推理过程中发生错误: NVML_SUCCESS == DriverAPI::get()->nvmlDeviceGetHandleByPciBusId_v2_( pci_id, &nvml_device) INTERNAL ASSERT FAILED at \"../c10/cuda/CUDACachingAllocator.cpp\":980, please report a bug to PyTorch. \n",
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      "推理过程中发生错误: NVML_SUCCESS == DriverAPI::get()->nvmlDeviceGetHandleByPciBusId_v2_( pci_id, &nvml_device) INTERNAL ASSERT FAILED at \"../c10/cuda/CUDACachingAllocator.cpp\":980, please report a bug to PyTorch. \n",
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      "推理过程中发生错误: NVML_SUCCESS == DriverAPI::get()->nvmlDeviceGetHandleByPciBusId_v2_( pci_id, &nvml_device) INTERNAL ASSERT FAILED at \"../c10/cuda/CUDACachingAllocator.cpp\":980, please report a bug to PyTorch. \n",
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      "['/gemini/data-2/images/00ec22e1-07bf-40f0-b45b-9384fb5bfc80-1235-0.jpg']\n",
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      "['/gemini/data-2/images/210448b917273244082093416d0faf-0.jpg']\n",
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      "['/gemini/data-2/images/212b97b217277396345648755d0fae-0.jpg']\n",
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      "['/gemini/data-2/images/21087db117278718607045727d0a76-0.jpg', '/gemini/data-2/images/21087db117278718607045727d0a76-1.jpg']\n",
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      "['/gemini/data-2/images/2166f9a617275339446141150d102d-0.png']\n",
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      "['/gemini/data-2/images/213f61c817272586962324474d0fa2-0.jpg', '/gemini/data-2/images/213f61c817272586962324474d0fa2-1.jpg']\n",
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      "推理过程中发生错误: NVML_SUCCESS == DriverAPI::get()->nvmlDeviceGetHandleByPciBusId_v2_( pci_id, &nvml_device) INTERNAL ASSERT FAILED at \"../c10/cuda/CUDACachingAllocator.cpp\":980, please report a bug to PyTorch. \n",
      "['/gemini/data-2/images/0bf8fa5617278342394073903d0ad8-0.jpg']\n",
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      "['/gemini/data-2/images/2135dcb317272666559548483d0fb7-0.jpg']\n",
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      "['/gemini/data-2/images/2133eecd17278691762347949d0fc0-0.jpg', '/gemini/data-2/images/2133eecd17278691762347949d0fc0-1.jpg']\n",
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      "['/gemini/data-2/images/2150a79317281025887018528d101e-0.jpg']\n",
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      "['/gemini/data-2/images/213d7d9e17277441945205351d83ae-0.png']\n",
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      "['/gemini/data-2/images/21329d1e17273663175024421d0fa2-0.jpg', '/gemini/data-2/images/21329d1e17273663175024421d0fa2-1.jpg']\n",
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      "['/gemini/data-2/images/212b885817274570340415146d0ae6-0.jpg']\n",
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      "['/gemini/data-2/images/213e5bd817272720540904701d0fd4-0.jpg', '/gemini/data-2/images/213e5bd817272720540904701d0fd4-1.jpg']\n",
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      "['/gemini/data-2/images/21673d0517273622384483943d102b-0.jpg']\n",
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      "['/gemini/data-2/images/2133780b17281137990263855d1000-0.jpg']\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "if data is not None:\n",
    "    for item in tqdm(data):\n",
    "        item_id = item.get('id', '')\n",
    "        image_path_for_item = ''\n",
    "        \n",
    "        print(item.get('image'))\n",
    "        print(len(item['image']))\n",
    "        if item.get('image') and len(item['image']) > 0:\n",
    "            response = get_model_response(\n",
    "                item_id, \n",
    "                item.get('instruction', ''), \n",
    "                os.path.join(image_path, item['image'][0])\n",
    "            )\n",
    "        else:\n",
    "            response = get_model_response(item_id, item.get('instruction', ''), \"\")\n",
    "        \n",
    "        expected_output = item.get(\"output\", '')\n",
    "        result = response == expected_output\n",
    "        \n",
    "        # 创建当前项的结果字典并立即保存到JSON文件\n",
    "        current_result = {\n",
    "            'item_id': item_id,\n",
    "            'image_path': image_path_for_item,\n",
    "            'instruction': item.get('instruction', ''),\n",
    "            'expected_output': expected_output,\n",
    "            'prediction': response,\n",
    "            'response_matches_expected': result\n",
    "        }\n",
    "        save_to_json(current_result)\n",
    "\n",
    "print(\"Results are being saved to the JSON file after each execution.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf9e6335-7b49-4995-ac2a-4f06ef2e0b09",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# from tqdm import tqdm\n",
    "# if data is not None:\n",
    "#     for item in tqdm(data):\n",
    "#         if item['id'] == \"21082e0317274845152512370d0a96\":\n",
    "#             print(len(item['image']))\n",
    "        \n",
    "#         if len(item['image']) > 0:\n",
    "#             response = get_model_response(item['id'], item['instruction'], os.path.join(image_path, item['image'][0]))\n",
    "#         else:\n",
    "#             response = get_model_response(item['id'], item['instruction'], \"\")\n",
    "#         expected_output = item[\"output\"]\n",
    "#         results.append(response == expected_output)\n",
    "#         # if response and \"output\" in response:\n",
    "#         #     results.append(response[\"output\"] == expected_output)\n",
    "#         # else:\n",
    "#         #     num_errors += 1\n",
    "#         # break  # 只处理一个项目以进行测试，实际应用中应移除此行\n",
    "\n",
    "# print(\"results: \", results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "679a8416-08f2-44bd-a95c-b7c5472377db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(614, 1000, 0.614)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(results), len(results), sum(results) / len(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70aa1f75-c1d1-4089-afac-3ee07d6e4ca6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0692c84-1a76-4755-9c68-9a1da6a8ab57",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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